I think importing spark.implicits._ where spark is actually the SparkSession will solve your error.
I don’t know if you have imported the implicit encoders?
Just follow the steps in this link:
Now, as far as I am aware nothing really changed since 1.6. And even if something is changed, your current code should work just fine with default encoders for product types.
To get some insights why your code worked in 1.x and may not work in 2.0.0 you'll have to check the signatures. In 1.x DataFrame.map is a method which takes function Row => T and transforms RDD[Row] into RDD[T].
In 2.0.0 DataFrame.map takes a function of type Row => T as well, but transforms Dataset[Row] (a.k.a DataFrame) into Dataset[T] hence T requires an Encoder. If you want to get the "old" behavior you should use RDD explicitly:
df.rdd.map(row => ???)
For Dataset[Row] map check this Encoder error while trying to map dataframe row to updated row